Speaker Diarization

Speaker diarization answers the question “who spoke when?” — given an audio clip with multiple speakers, it returns time-stamped segments labelled with a stable speaker ID (SPEAKER_00, SPEAKER_01, …).

LocalAI exposes this through the /v1/audio/diarization endpoint, modelled after /v1/audio/transcriptions. Two backends are supported today:

sherpa-onnx — pyannote-3.0 segmentation + a speaker-embedding extractor (3D-Speaker, NeMo, WeSpeaker) + fast clustering. Pure diarization — no transcription cost. Recommended when you only need speaker turns.
vibevoice.cpp — produces speaker-labelled segments as a by-product of its long-form ASR pass, so you can optionally get a transcript per segment for free.

Because diarization is exposed as a regular OpenAI-compatible endpoint, any HTTP client works. There is no Python dependency on pyannote or NeMo on the consumer side.

Endpoint

POST /v1/audio/diarization
Content-Type: multipart/form-data

Field	Type	Description
`file`	file (required)	audio file in any format `ffmpeg` accepts
`model`	string (required)	name of the diarization-capable model
`num_speakers`	int	exact speaker count when known (>0 forces; 0 = auto)
`min_speakers`	int	hint when auto-detecting
`max_speakers`	int	hint when auto-detecting
`clustering_threshold`	float	cosine distance threshold used when `num_speakers` is unknown
`min_duration_on`	float	discard segments shorter than this many seconds
`min_duration_off`	float	merge gaps shorter than this many seconds
`language`	string	only meaningful for backends that bundle ASR (e.g. vibevoice)
`include_text`	bool	when the backend can emit per-segment transcript for free, populate it
`response_format`	string	`json` (default), `verbose_json`, or `rttm`

Response — `json` (default)

Compact payload, no transcription, no per-speaker summary:

{
  "task": "diarize",
  "duration": 12.34,
  "num_speakers": 2,
  "segments": [
    {"id": 0, "speaker": "SPEAKER_00", "label": "0", "start": 0.00, "end": 2.34},
    {"id": 1, "speaker": "SPEAKER_01", "label": "1", "start": 2.34, "end": 4.10}
  ]
}

speaker is the normalized, zero-padded label clients should display. label preserves the raw backend-emitted ID for clients that maintain their own speaker dictionary.

Response — `verbose_json`

Adds per-speaker totals and (when the backend supports it and include_text=true) the per-segment transcript:

{
  "task": "diarize",
  "duration": 12.34,
  "language": "en",
  "num_speakers": 2,
  "segments": [
    {"id": 0, "speaker": "SPEAKER_00", "label": "0", "start": 0.00, "end": 2.34, "text": "Hello, world."},
    {"id": 1, "speaker": "SPEAKER_01", "label": "1", "start": 2.34, "end": 4.10, "text": "How are you?"}
  ],
  "speakers": [
    {"id": "SPEAKER_00", "label": "0", "total_speech_duration": 5.6, "segment_count": 3},
    {"id": "SPEAKER_01", "label": "1", "total_speech_duration": 1.76, "segment_count": 1}
  ]
}

Response — `rttm`

NIST RTTM, the standard interchange format used by pyannote.metrics / dscore:

SPEAKER audio 1 0.000 2.340 <NA> <NA> SPEAKER_00 <NA> <NA>
SPEAKER audio 1 2.340 1.760 <NA> <NA> SPEAKER_01 <NA> <NA>

Returned as Content-Type: text/plain; charset=utf-8.

Quick start

curl http://localhost:8080/v1/audio/diarization \
  -H "Content-Type: multipart/form-data" \
  -F file="@meeting.wav" \
  -F model="pyannote-diarization" \
  -F num_speakers=3

Backend setup — sherpa-onnx (pure diarization)

Sherpa-onnx needs two ONNX models: pyannote segmentation and a speaker-embedding extractor. Place them under your LocalAI models directory and reference them from the YAML:

name: pyannote-diarization
backend: sherpa-onnx
type: diarization
parameters:
  model: sherpa-onnx-pyannote-segmentation-3-0/model.onnx
options:
  - diarize.embedding_model=3dspeaker_speech_campplus_sv_zh-cn_16k-common.onnx
  # Optional clustering knobs (per-call DiarizeRequest fields override these):
  - diarize.threshold=0.5
  - diarize.min_duration_on=0.3
  - diarize.min_duration_off=0.5
known_usecases:
  - FLAG_DIARIZATION

Both model: and diarize.embedding_model= are resolved relative to the LocalAI models directory.

Backend setup — vibevoice.cpp (diarization + ASR)

vibevoice.cpp’s ASR mode emits [{Start, End, Speaker, Content}] natively, so a single pass gives both diarization and transcription:

name: vibevoice-diarize
backend: vibevoice-cpp
parameters:
  model: vibevoice-asr.gguf
options:
  - type=asr
  - tokenizer=vibevoice-tokenizer.gguf
known_usecases:
  - FLAG_DIARIZATION
  - FLAG_TRANSCRIPT

Pass include_text=true on the request to populate the text field on each diarization segment.

curl http://localhost:8080/v1/audio/diarization \
  -H "Content-Type: multipart/form-data" \
  -F file="@interview.wav" \
  -F model="vibevoice-diarize" \
  -F include_text=true \
  -F response_format=verbose_json

Notes

Speaker identity across files: speaker IDs (SPEAKER_00, SPEAKER_01, …) are local to each request. To track the same person across multiple recordings, combine /v1/audio/diarization with /v1/voice/embed (speaker embedding) and maintain your own embedding store.
Hints vs. forces: num_speakers overrides clustering when set; min_speakers / max_speakers are advisory and only honored by backends that expose a range hint. vibevoice.cpp ignores them — its model picks the count itself.
Sample rate: input is automatically converted to 16 kHz mono via ffmpeg before the backend sees it; sherpa-onnx pyannote-3.0 requires 16 kHz.